HEDGEHOG: Hierarchical Evaluation of Drug Generators Through Rigorous Filtration

2026-07-14

AI Summary

Current evaluation metrics for molecular generation models often fail to reflect the practical viability of compounds in drug discovery, leading to a high rate of false positives. To address this limitation, this work proposes HEDGEHOG—a six-stage cascaded filtering benchmark that emulates industrial hit identification workflows by incorporating multidimensional constraints, including physicochemical properties, structural plausibility, synthetic accessibility, molecular docking, and three-dimensional conformational stability. HEDGEHOG establishes the first unified, hierarchical evaluation framework closely aligned with real-world drug discovery practices. When applied to 230,000 generated molecules, only 0.65% passed all screening stages, exposing a fundamental shortcoming of existing models in simultaneously satisfying multiple realistic constraints. This benchmark provides a more rigorous and reliable standard for assessing molecular generation methods.

Abstract

Generative molecular models can support early drug discovery by proposing new candidate compounds de novo. In practice, useful candidates must balance target-relevant activity, synthetic accessibility, physicochemical properties, and other multiparameter design constraints. However, metrics commonly used to evaluate molecular generators only weakly reflect whether the generated compounds are medicinally plausible and suitable for downstream computation. This can produce false positives in model evaluation, incorrect assumptions, and inefficient use of computational resources. We introduce HEDGEHOG, a unified six-stage filtration benchmark that is inspired by industrial hit identification workflows: (i) preprocessing; (ii) physicochemical descriptor screening; (iii) structural alerts and graph-sanity checks; (iv) synthesis feasibility; (v) docking and binding affinity estimation; and (vi) three-dimensional pose and interaction checks. We evaluate 23 molecular generators across three model classes under a standardized protocol. Across 230,000 generated molecules, only 0.65% of initial molecules survive all stages. Our results expose a central limitation of current molecular generators: molecules that appear acceptable under isolated criteria rarely satisfy medicinal chemistry, synthesis, docking, and 3D pose filters simultaneously.

Problem

• molecular generators
• drug discovery
• evaluation metrics
• medicinal plausibility
• multiparameter design

Innovation

• molecular generation
• multi-stage filtration
• drug-likeness evaluation
• synthetic feasibility
• 3D pose validation